Economic Disparities in Utilization and Outcomes of Structural Heart Disease Interventions in the United States

Background Disparities in access to care cause negative health consequences for underserved populations. Economic disparities in structural heart disease (SHD) interventions are not well characterized. Objectives The objective of this study was to evaluate economic disparities in the utilization and outcomes of SHD interventions in the United States. Methods We queried the National Inpatient Sample (2016-2020) to examine economic disparities in the utilization, in-hospital outcomes, length of stay, and cost of SHD interventions among patients ≥65 years of age. Outcomes were determined using logistic regression models. Results A total of 401,005 weighted hospitalizations for transcatheter aortic valve replacement, left atrial appendage occlusion, transcatheter mitral valve repair, and transcatheter mitral valve replacement were included. Utilization rates (number of procedures performed per 100,000 hospitalizations) were higher in patients with high income compared with medium and low income for transcatheter aortic valve replacement (559 vs 456 vs 338), left atrial appendage occlusion (148 vs 136 vs 99), transcatheter mitral valve repair (65 vs 54 vs 41), and transcatheter mitral valve replacement (7.7 vs 6.7 vs 1.2) (all P < 0.01). Low- and medium-income patients had distinctive demographic and clinical risk profiles compared with high-income patients. There were no significant differences in the adjusted in-hospital mortality, key complications, or length of stay between high-, medium-, and low-income patients following any of the 4 SHD interventions. High-income patients incurred a modestly higher cost with any of the 4 SHD interventions compared with medium- and low-income patients. Conclusions Economic disparities exist in the utilization of SHD interventions in the United States. Nonetheless, adjusted in-hospital outcomes were comparable among high-, medium-, and low-income patients. Multifaceted implementation strategies are needed to attenuate these utilization disparities.

S tructural heart disease (SHD) repre- sents a spectrum of noncoronary conditions associated with significant morbidity and mortality. 1[4] Some inequities in access to SHD interventions are well-documented. 5While prior studies have evaluated racial and ethnic disparities in SHD interventions, 6,7 data on the impact of economic disparities on the utilization and outcomes of SHD  9 The NIS is compiled annually, which allows for analysis of procedural trends over time. 10This study was exempt from the requirements of the Mayo Clinic Institutional Review Board because the NIS is a publicly available database composed of deidentified data.Continuous variables were compared using the Kruskal-Wallis 1-way analysis of variance.
A multivariable logistic regression analysis was constructed to adjust for potential confounders including age, sex, race, insurance, hospital location and teaching status, bed size, region, type of admission (elective/nonelective and weekend/weekday), Elixhauser and Charlson comorbidity index scores, and relevant comorbidities (Supplemental Table 2).Adjustment variables were selected a priori on the basis of their clinical significance, which may directly influence in-hospital outcomes.The results from these models are presented as adjusted ORs (aORs) with 95% CIs.Race was evaluated as an effect modifier for both utilization and outcomes of SHD interventions, and the results are presented separately for White and non-White patients along with the respective interaction P value.Trend analyses from 2016 through 2020 were conducted using linear regression.
In accordance with the HCUP data use agreement, we did not report variables that contained a small number of observed (ie, unweighted) hospitalizations (<11) as this could pose risks of subject identification and data privacy violation. 13A 2-tailed P < 0.05 was considered statistically significant.All statistical analyses were performed using Stata, version 17 (Stata-Corp) software, accounting for the NIS sampling design, and were weighted using sampling weights provided with the NIS database to estimate nationallevel effects per HCUP-NIS recommendations. 10

UTILIZATION RATE OF SHD INTERVENTIONS PER INCOME GROUP.
A total of 401,005 weighted hospitalizations for SHD interventions were identified for analysis.In the overall cohort, patients in the highincome group constituted 26.7%, 24.4%, 26.5%, and 26.3% of all patients who underwent TAVR, LAAO, TMVr, and TMVR, respectively.The utilization rates (number of procedures/100,000 hospitalizations in patients aged $65 years) were significantly higher in patients with high income compared with medium and low income for all 4 procedures: TAVR (559 vs 456 vs 338), LAAO (148 vs 136 vs 99), TMVr (65 vs 54 vs 41), and TMVR (7.7 vs 6.7 vs 1.2) (all P < 0.01; Central Illustration).Race did not significantly modify the effect of income on utilization of SHD interventions (all interaction P > 0.05) (Figure 2).Low-income patients undergoing TAVR, LAAO, and TMVr had higher Elixhauser and Charlson comorbidity index scores compared with high-income patients, mainly driven by the higher rates of Ismayl et al

In multivariable regression analysis, low income
Economic Disparities in SHD Interventions  3 and 4. Race did not significantly modify the     3 to 6).   Likewise, the number of TMVR procedures increased significantly in all racial/ethnic and sex groups from 2016 to 2020. 15,24Increased SHD interventions across all income groups can be attrib-

<0.01
Values are %, OR (95% CI), or median (IQR).The bold values indicate statistical significance.The International Classification of Diseases-10th Revision (ICD-10) codes corresponding to each of the in-hospital outcomes were identified with the same process used to identify comorbidity codes (Supplemental Table 1).a The multivariable regression model is adjusted for age, sex, race/ethnicity, insurance, hospital location and teaching status, bed size, region, type of admission, Elixhauser and Charlson comorbidity index scores, and relevant comorbidities (Supplemental Table 2).b Cell counts <11 are not reportable (NR) per HCUP guidelines.Multifaceted implementation strategies are needed to attenuate these utilization disparities.USA.E-mail: ismayl.mahmoud@mayo.edu.

R E F E R E N C E S
interventions remain scarce.Therefore, we queried the National Inpatient Sample (NIS) database to investigate economic disparities in the utilization and outcomes of common SHD interventions.METHODS DATA SOURCE AND ETHICS STATEMENT.Hospitalization data were abstracted from the NIS database, which is part of the Healthcare Cost and Utilization Project (HCUP) family of databases sponsored by the Agency for Healthcare Research and Quality. 8The NIS is the largest publicly available, fully deidentified, allpayer inpatient healthcare database in the United States.The NIS is derived from billing data submitted by hospitals to statewide organizations across the United States and has reliable and verified patient linkage numbers that can be used to track patients across hospitals within each state while adhering to strict privacy guidelines.The NIS database contains both patient-and hospital-level information from approximately 1,000 hospitals and represents approximately 20% of all U.S. hospitalizations, covering >7 million unweighted hospitalizations each year.When weighted, the NIS extrapolates to the national level, representing 35 million hospitalizations each year.Up to 40 discharge diagnoses and 25 procedure codes are collected for each patient using International Classification of Diseases-10th Revision codes.
STUDY SAMPLE AND PATIENT SELECTION.We queried the NIS database from January 2016 through December 2020 to identify hospitalizations in which patients $65 years of age underwent TAVR, percutaneous LAAO, TMVr, or TMVR using International Classification of Diseases-10th Revision, Procedure Coding System codes (02RF37H, 02RF38H, 02RF3JH, 02RF3KH, X2RF332, 02RF37Z, 02RF38Z, 02RF3JZ, 02RF3KZ for TAVR; 02L73DK for percutaneous LAAO; 02UG3JH, 02UG3JZ, 02QG3ZE, 02QG3ZZ for TMVr; and 02RG37H, 02RG38H, 02RG3JH, 02RG3KH, 02RG37Z, 02RG38Z, 02RG3JZ, 02RG3KZ for TMVR).A complete list of International Classification of Diseases-10thRevision diagnosis and procedure codes used in this study is presented in Supplemental Table1.We excluded hospitalizations in which the patient was aged <65 years as well as those with missing data on income (Figure1).We selected age 65 as a cutoff to minimize the potential confounding issue of lack of insurance coverage, given that most patients $65 years of age are eligible for the Centers of Medicare and Medicaid Services.Hospitalizations with >1 SHD intervention during the same admission were excluded from the outcomes analysis but not from the utilization and trend analyses.The estimated median household incomes are ZIP code-specific, updated annually, and classified into 4 quartiles indicating the wealthiest to poorest populations.Similar to prior NIS studies, for hospitalizations that met inclusion criteria, we stratified the patients into 3 cohorts based on income: high income (75th-100th percentile), medium income (25th-75th percentile), and low income (0th-25th percentile). 11A more detailed explanation of all the variables in the NIS, including the specific dollar amounts in each category of median household income, is available on the HCUP website (https://www.hcup-us.ahrq.gov/db/nation/nis/nisdde.jsp).STUDY OUTCOMES.The primary outcome was procedure utilization rate per income group defined as the number of procedures performed per 100,000 hospitalizations in patients aged $65 years.Secondary outcomes included temporal trends in SHD interventions in different income groups across the study period, as well as in-hospital complications including major adverse cardiovascular events (defined as a composite of all-cause in-hospital mortality or stroke), all-cause in-hospital mortality, A B B R E V I A T I O N S A N D A C R O N Y M S HCUP = Healthcare Cost and Utilization Project LAAO = left atrial appendage occlusion LOS = length of stay NIS = National Inpatient Sample SHD = structural heart disease TAVR = transcatheter aortic valve replacement TMVr = transcatheter mitral valve repair TMVR = transcatheter mitral valve replacement Ismayl et al J A C C : A D V A N C E S , V O L . 3 , N O .7 , 2 0 2 4 Economic Disparities in SHD Interventions J U L Y 2 0 2 4 : 1 0 1 0 3 4 stroke, acute kidney injury, major bleeding, vascular complications (defined as a composite of arteriovenous fistula, aneurysm, hematoma, retroperitoneal bleeding, or venous thromboembolism), permanent pacemaker placement, and cardiac tamponade.We also evaluated hospital length of stay (LOS), total hospital costs (inflation-adjusted to 2020 U.S. dollars 12 ), and discharge disposition.Charge-to-cost ratio files were used to convert charges to costs at the individual hospital level.STATISTICAL ANALYSIS.Descriptive statistics were presented as percentages for categorical variables and as median (IQR) for continuous variables.Categorical variables were compared using the Pearson chisquare test or Fisher exact test as appropriate.

FIGURE 3
FIGURE 3 Year-Over-Year Trend in the Number of SHD Interventions Performed in Patients Aged ‡65 Years in the U.S. Stratified by Economic Status BASELINE RISK PROFILE AMONG INCOME GROUPS.There were notable differences in demographic, hospital, and clinical characteristics among patients undergoing SHD interventions across the different income groups.Low-income patients were younger and more likely to be female, Black or Hispanic, and receive care in rural hospitals with small bed sizes compared with high-income patients.Regional variations were also observed, with low-income patients undergoing SHD interventions more commonly in the South and Midwest and less commonly in the Northeast and West compared with highincome patients.
diabetes mellitus, obesity, coronary artery disease, congestive heart failure, dialysis dependence, and chronic pulmonary disease.High-income patients were more likely to have cancer.Baseline characteristics stratified by income group are shown in Tables1 and 2. IN-HOSPITAL OUTCOMES, LOS, AND COSTS OF SHD INTERVENTIONS STRATIFIED BY INCOME GROUP.Despite the underutilization of SHD interventions in low-and medium-income groups, no significant differences in in-hospital outcomes were observed.With TAVR, there were no statistically significant differences in in-hospital mortality or key post-TAVR complications among the 3 income groups after adjustment for potential confounders.Similarly, inhospital outcomes following LAAO, TMVr, and TMVR were similar across all income groups after adjustment for potential confounders.Although the hospital LOS associated with SHD interventions was similar among the 3 groups, the costs of all 4 procedures were significantly higher in high-income patients.In-hospital outcomes, LOS, and costs of SHD interventions stratified by income group are shown in Tables

J
A C C : A D V A N C E S , V O L . 3 , N O .7 on in-hospital outcomes, LOS, and costs of SHD interventions (all interaction P > 0.05) (Supplemental Tables We report economic disparities in the utilization and outcomes of SHD interventions in the United States.This analysis of the large, nationally representative NIS database produced several novel findings: 1) common SHD interventions are performed less frequently in low-and medium-income patients compared with high-income patients; 2) from 2016 through 2020, the use of SHD interventions increased significantly in all 3 income groups; 3) significant differences exist in demographic, hospital, and clinical characteristics among high-, medium-, and lowincome patients who undergo SHD interventions in contemporary U.S. practice; and 4) in-hospital mortality, morbidity, and LOS of common SHD interventions were comparable across the 3 income groups, but the cost was higher in high-income patients.

JEconomic
A C C : A D V A N C E S , V O L . 3 , N O .7 Disparities in SHD Interventions sizes compared with high-income patients.Centers of SHD care are not uniformly distributed geographically; instead they are clustered in large cities with large academic medical centers often serving affluent populations.This disparity has been present since the dawn of SHD interventions, with Nathan et al.17 reporting that, during the initial growth phase of TAVR programs in the United States, hospitals serving wealthier patients were more likely to start TAVR programs.This pattern of growth has led to inequities in the dispersion of TAVR, with lower rates in poorer communities.17 Multifaceted implementation strategies are needed to attenuate economic disparities in the utilization of SHD interventions.Providers should undergo training to recognize and address implicit biases in the care of patients with SHD, as these unconscious biases often influence clinical decisions inadvertently, perpetuating disparities in SHD interventions. 18Continued work to increase the diversity of the physician workforce is needed to mitigate socioeconomic disparities in SHD interventions, as prior data have shown racial concordance between patients and providers to be associated with improved healthcare outcomes, driven by more effective communication and shared decision-making. 19,20"Safety net hospitals," which predominantly serve low-and middleincome patients, often face significant resource limitations in SHD care, which are further aggravated by geographical disparities in access to SHD interventions. 21Improving access to SHD interventions at safety net hospitals is therefore essential to addressing disparities in SHD care. 21This improved access will require increased resources through federal, state, and local funding and grants, promoting peer-to-peer training in SHD interventions for providers in underserved communities, and involving safety net hospitals in clinical trials of SHD therapies. 21Improved resources in safety net hospitals may also attract interventional cardiologists trained in SHD interventions, thus making these treatments more accessible.Governmental and grant funding to facilitate the acquisition of advanced interventional and imaging technologies used in SHD interventions will reduce barriers to SHD care and cultivate training pathways for SHD intervention and imaging in safety net hospital communities. 21,22Finally, increasing healthcare literacy in communities with low socioeconomic status through educational programs, healthcare advocacy, and community outreach may further reduce disparities in access to SHD interventions.TEMPORAL TRENDS IN SHD INTERVENTIONS.Despite the underutilization of SHD interventions among low-and medium-income groups, our study found a temporal increase in SHD interventions performed in all 3 income groups.These increases are congruent with a prior study evaluating trends in SHD interventions that noted that the number of TAVR, LAAO, and mitral transcatheter edge-to-edge repair procedures increased significantly from 2016 to 2020.

uted to: 1 ) 4 Economic
improved safety, efficacy, and feasibility of catheter-based SHD interventions, compared to surgical methods, as a result of rapid advancements in device technology and techniques as well as enhanced operator experience; 25,26 and 2) broadened indications for SHD interventions. 3,26IN-HOSPITAL OUTCOMES, LOS, AND COSTS.Our study found similar in-hospital outcomes among patients undergoing SHD interventions across all income groups.These reassuring data are in line with prior studies showing comparable adjusted inhospital outcomes of LAAO between income quartiles. 27Nonetheless, further studies evaluating the long-term outcomes of SHD interventions in different income groups are still warranted.Hospitalization costs were significantly higher among high-income patients compared to low-and medium-income patients undergoing SHD interventions.This cost gradient is congruent with work by Sparrow et al,27 which reported that as income quartile increased, LAAO hospitalization costs increased significantly.Regional variations may contribute to the discrepancy in hospitalization costs, as prior studies have demonstrated lower hospitalization costs in the Midwest and South regions, where incomes were lower among SHD intervention patients in our study, and higher hospitalization costs in the Northeast and West, where incomes were higher among SHD intervention patients.28STUDY LIMITATIONS.Our study has several important limitations.First, in a retrospective NIS study using administrative claims codes, incorrect coding could lead to inaccurate data.Second, the retrospective nature of the study makes it subject to inherent selection bias.Third, detailed baseline and procedural characteristics such as echocardiographic findings, access site, and peri-procedural medications were Ismayl et al J A C C : A D V A N C E S , V O L . 3 , N O .7 , 2 0 2 aOR ¼ adjusted odds ratio; ICF ¼ intermediate care facility; LAAO ¼ left atrial appendage occlusion; LOS ¼ length of stay; MACE ¼ major adverse cardiovascular events; PPM ¼ permanent pacemaker; SNF ¼ skilled nursing facility; TAVR ¼ transcatheter aortic valve replacement; uOR ¼ unadjusted odds ratio.Despite these limitations, this study adds mean-ingfully to the literature by describing contemporary economic disparities in the utilization and outcomes of SHD interventions.The NIS is well validated for outcomes studies like this one and undergoes serial data accuracy checks and quality control.In addition, the NIS data are geographically diverse, including a nationally representative sample of centers and operators, and hence reliably reflect real-world practice and outcomes.CONCLUSIONS Economic disparities exist in the utilization of SHD interventions in the United States.Nonetheless, adjusted in-hospital outcomes were comparable among high-, medium-, and low-income patients.
This work was supported by the Department of Cardiovascular Medicine at Mayo Clinic in Rochester, Minnesota, USA.Dr Goldsweig reports consulting for Philips and Inari Medical; and speaking for Philips and Edwards Lifesciences.All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.ADDRESS FOR CORRESPONDENCE: Dr Mahmoud Ismayl, Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, Minnesota 55905,

TABLE 1
Baseline Characteristics of Patients Undergoing TAVR and LAAO Stratified by Income

TABLE 1
Values are median (IQR) or %.Two authors (M.I. and H.A.) independently verified the International Classification of Diseases-10th Revision (ICD-10) codes that corresponded to each comorbidity (Supplemental Table1), and any disagreements in inclusion or exclusion of ICD-10 codes were discussed with a third author (A.M.G). a Other includes Asian or Pacific Islander, Native American, and Other.
b Bed-size categories are based on inpatient beds and are specific to the hospital's location and teaching status.CABG ¼ coronary artery bypass grafting; ICD ¼ implantable cardioverter-defibrillator; LAAO ¼ left atrial appendage occlusion; PCI ¼ percutaneous coronary intervention; PPM ¼ permanent pacemaker; TAVR ¼ transcatheter aortic valve replacement; TIA ¼ transient ischemic attack.

TABLE 2
Baseline Characteristics of Patients Undergoing TMVr and TMVR Stratified by Income Continued on the next page

TABLE 2
Values are median (IQR) or %.Two authors (M.I. and H.A.) independently verified the International Classification of Diseases-10th Revision (ICD-10) codes that corresponded to each comorbidity (Supplemental Table1), and any disagreements in inclusion or exclusion of ICD-10 codes were discussed with a third author (A.M.G). a Other includes Asian or Pacific Islander, Native American, and Other.b Cell counts <11 are not reportable (NR) per HCUP guidelines.c Bed-size categories are based on inpatient beds and are specific to the hospital's location and teaching status.CABG ¼ coronary artery bypass grafting; ICD ¼ implantable cardioverter-defibrillator; PCI ¼ percutaneous coronary intervention; PPM ¼ permanent pacemaker; TIA ¼ transient ischemic attack; TMVr ¼ transcatheter mitral valve repair; TMVR ¼ transcatheter mitral valve replacement.

TABLE 3
In-Hospital Outcomes of TAVR and LAAO Stratified by Income